Course
Description:
Theory and practice of linear regression, analysis of variance (ANOVA)
and their extensions, including testing, estimation, confidence
interval procedures, modeling, regression diagnostics and plots,
polynomial regression, colinearity and confounding, model selection,
geometry of least squares. The theory will be approached mainly from
the frequentist perspective and use of the computer (mostly R) to
analyze data will be emphasized. We will cover most of the material
corresponding to the first 11 chapters of the required text, along with
some supplementary material where needed.

Course Goals:
By the end of the course, students should demonstrate knowledge of the
theory underlying linear statistical models, as well as some competence
in applying the theory to the analysis of data using R. Students should
understand the limitations and implications of key assumptions of
linear models, and have a working knowledge of common methods of
estimation, hypothesis testing and model diagnostics for linear models.

Prerequisites:
Math 3200 and a course in linear algebra, or permission of instructor.